In the manufacture of semiconductor wafers, plasma etching is a process for transferring circuit patterns from the surface of a semiconductor wafer to an underlying layer by using a highly reactive ionized gas to remove unmasked portions of the wafer. As in most manufacturing processes, quality control of a desired product attribute is necessary. In integrated circuit ("IC") manufacturing a key wafer attribute is the post etch thickness of a film on the wafer. Overetching can adversely effect the performance of the final device.
Plasma etch processing can result in variances in post etch film thickness from lot to lot (for example, 10 to 50 wafers processed in sequence) and wafer to wafer for several reasons. One source for these variations is the fluctuations over time of the variables which control the plasma etch machine, such as applied RF power, gas pressure and gas flow rates. The age of the reactor is another factor known to affect the etch time for a desired thickness. In addition, variations in the pattern densities for different wafers also affect the final film thickness.
One prior art method for controlling film thickness is to inspect and measure the etch of prior lots of wafers and adjust the etch time for subsequent lots. This obviously entails interrupting production between lots, manual measurements of a sample and statistical analysis. In addition to interrupting the manufacturing process, this method fails to monitor the process in real time and does not provide a means for determining film thickness on an individual wafer.
A more recent and sophisticated control device and method is described in Edward A. Rietman et at., Active Neural Network Control of Wafer Attributes in a Plasma Etch Process, 11 J. Vac. Tech. B at 1314 (1993), incorporated as if set forth fully herein, in which a neural network is used to predict the ideal etch time for a desired thickness. The authors show that a relationship exists between the optical emission trace from the plasma glow and the ideal etch time for a desired film thickness. The neural network is trained to learn the relationship between the emission trace over a set period of time and the optimum etch time for a desired thickness. After training, optical emission measurements can be entered into the neural network which then predicts the ideal etch time for the current wafer being processed. The emission trace measurements are collected for approximately one third of the total expected etch time so that sufficient time remains to input the data to the neural network and receive from it the predicted ideal etch time long before the desired thickness is obtained.
In further work for predicting ideal etch time for wafers with ever smaller circuit features, Rietman et at., demonstrated that inputting to the neural network the mean value of fluctuations for various variables of the prior and current plasma etch process in addition to the emission trace measurements, enhances the convergence of the neural network. Edward A. Rietman et at., Neural Network Control Of a Plasma Gate Etch: Early Steps in Wafer-To-Wafer Process Control, Int'l. Electronics Manufacturing, Technology Symposium at 454 (IEEE/CHMT 1993) incorporated as if set forth fully herein. The input variables for the current wafer include: applied and reflected RF power; de bias; gas flow rates; reactor pressure; and time to generate the emission trace. The input variables of the prior lot include: observed thickness; observed etch time; applied RF power; and dc bias.
The neural network methods described above represent a significant technological step forward in controlling oxide film thickness. By eliminating human intervention and collecting data in situ, the etching process is allowed to continue from lot to lot without interruption. Moreover, a method is available for determining the ideal etch time for a particular wafer being processed based on data obtained from the current wafer and previous lot.
Notwithstanding its advances, the above methods use process signatures from a point in time, t, and time delayed signals to predict a future time, t+i, when the desired thickness will be reached. In situ monitoring of the wafer attributes in real-time, i.e. determining at time t the present film thickness, line width or line profile, as well as other attributes, is available only with expensive diagnostics. The subject invention provides a method and apparatus whereby the state of a wafer attribute, such as film thickness, line width or line profile, can be monitored in real time by using intelligent systems, such as neural networks, trained in the relationship between signatures representing the changing process variables, process state changes, and signatures representing the state of the wafer attribute. This approach avoids the use of expensive on-line diagnostics.